Intelligence related differences in induced brain activity during the performance of memory tasks

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Intelligence related differences in induced brain activityduring the performance of memory tasks

Norbert Jausovec*, Ksenija Jausovec

Univerza v Mariboru, Pedagoska fakulteta, Koroska 160, Slovenia

Received 25 July 2002; received in revised form 1 January 2003; accepted 21 February 2003

Abstract

The aim of this study was to investigate the conflicting results in event-related desynchronization/syn-chronization (Pfurtscheller, 1999) in the upper alpha and theta bands related to intelligence. Two groups ofindividuals (high intelligent MIQ=128, and average intelligent MIQ=93), solved two memory tasks whiletheir EEG was recorded. The data were analyzed with the ERD/ERS method and low resolution brainelectromagnetic tomography (LORETA). In the upper alpha band high intelligent individuals displayedmore desynchronization than did average intelligent individuals. In the theta band a minor but reverseeffect was observed. The LORETA analysis indicated that average intelligent individuals displayed muchhigher signal to noise ratios (SNR) than did high intelligent individuals. The EEG source location for thehigh intelligent group was more anterior, whereas for the average intelligent group a more posterior EEGsource location was observed. The findings were explained in the light of the neural efficiency model.# 2003 Elsevier Ltd. All rights reserved.

Keywords: Verbal and performance intelligence; Emotional intelligence; Induced band power; ERD/ERS; LORETA

1. Introduction

A considerable body of evidence from ongoing electroencephalogram (EEG), event relatedpotentials (ERP), and positron emission tomography (PET) studies has demonstrated a negativecorrelation between brain activity under cognitive load and intelligence (Anokhin, Lutzenberger,& Birbaumer, 1999; Haier & Benbow, 1995; Haier et al., 1988; Haier, Siegel, Tang, Abel, &Buchsbaum 1992; Jausovec, 1996, 1998, 2000; Jausovec & Jausovec 2000a, 2000b, 2001; Lutzen-berger, Birbaumer, Flor, Rockstroh, & Elbert., 1992; Neubauer, Freudenthaler, & Pfurtscheller,

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doi:10.1016/S0191-8869(03)00120-X

Personality and Individual Differences 36 (2004) 597–612

* Corresponding author. Tel.: +362-2-2293-606; fax: +386-2-258180.

E-mail address: [email protected] (N. Jausovec).

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1995; Neubauer, Sange, & Pfurtscheller, 1999; O’Boyle, Benbow, & Alexander, 1995). Theexplanation of these findings was an efficiency theory. This efficiency may derive from the non-useof many brain areas irrelevant for good task performance as well as the more focused use ofspecific task-relevant areas in high intelligent individuals.In a series of studies by Klimesch (Doppelmayr, Klimesch, Stadler, Pollhuber, & Heine, 2002;

Klimesch, 1999; Klimesch & Doppelmayr, 2001), employing the method of ERD/ERS, usingindividually determined narrow frequency bands (2 Hz), it was found that upper alpha desyn-chronization was larger for good memory performers as compared with bad performers. Theopposite holds true for the theta band, where good memory performance was reflected by a largeextent of synchronization. Alpha amplitude tends to decrease (desynchronization) with increasesin mental effort, while theta band amplitude tends to increase (synchronization; Nunez, Wingeier,& Silberstein, 2001). These results are the opposite of what would be predicted by the efficiencytheory.Two reasons may have accounted for the conflicting results. First, the complex dynamics of

EEG rhythms which are still poorly understood, and second, the compound structure of intelli-gence, which serves as an umbrella concept that includes several groups of mental abilities.Many earlier broadband studies of alpha frequency have compounded everything in the 8–13

Hz band into a single measure, thereby failing to note important cognitive effects. Analyzing thedynamics of narrow frequency bands (1 or 2 Hz) has shown that multiple rhythms interact tovarying degrees in different brain states. A possible explanation for the conflicting results could,therefore, be that increases in upper alpha indicate a specific memory processing function and notsimple idling. Nunez et al. (2001) have proposed a local-global theory in which global modula-tions in synaptic action in multiple frequency bands (especially alpha) cooperate with localoscillatory dynamics. This implies the formation of local alpha and theta networks partly distinctfrom, but perhaps interacting with, global alpha rhythms. Klimesch (Doppelmayr et al., 2002;Klimesch, 1999, Klimesch & Doppelmayr, 2001) made a distinction between tonic and phasic (orevent-related) EEG power. Phasic changes are task related and occur in a rapid rate, whereastonic changes are not under volitional control and occur at a much slower rate. Klimesch sug-gested that only if there is sufficient activity in the upper alpha band during a reference interval(resting state–tonic power) would there be a possibility of a large extent of power suppressionduring task performance. The opposite holds true for the theta band, where low reference poweris related to a large amount of synchronization or increase in power. It was further suggested thatthis difference, reflecting the extent of a phasic change, is related to a physiological mechanismwhich operates to increase the SNR during task performance. A recent study by Jausovec andJausovec (2001) also points in this direction. Differences in current density related to intelligencewere analyzed with LORETA. In high intelligent individuals a decrease in the volume of acti-vated cortical gray matter between the P300 onset and the P300 peak amplitude accompanied byan increase in current density was observed. The EEG of low intelligent individuals showed areverse pattern of cortical activity.One of the earliest divisions of intelligence splits it into verbal, performance and social intelli-

gence (Thorndike, 1920). Similarly most contemporary models of intelligence view intelligence asa complex system that includes interactions between mental processes, contextual influences, andmultiple abilities [e.g. Sternberg’s (2000) triarchic theory, Gardner’s (1988) theory of multipleintelligences, and Ceci’s (1996) bioecological theory]. As stressed by Gevins and Smith (2000) a

598 N. Jausovec, K. Jausovec / Personality and Individual Differences 36 (2004) 597–612

large number of the ERP and EEG studies mentioned have used tasks that made only trivialdemands on cognitive abilities. Hence, the majority of past results are somewhat equivocal withrespect to interpretation in terms of these constructs. In a recent study by Gevins and Smith(2000), who tested students under more challenging task conditions, no significant relationshipsbetween the absolute power of the theta and alpha signals and IQ scores could be identified.Several studies have also reported a greater relationship between EEG and ERP measures withspecific rather than with general abilities (Batt, Nettelbeck, & Cooper, 1999; Duffy, McAnulty, &Waber, 1999). It was further suggested that creative and intelligent individuals differ in the neu-rological activity displayed while solving problems (Carlsson, Wendt, & Risberg, 2000; Jausovec,2000). A similar finding was also reported for emotional intelligence (Jausovec, Jausovec, &Gerlie, 2001), and extraversion (Fink, Schrausser, & Neubauer, 2002). From this viewpoint theselection of individuals exclusively on the ground of verbal and performance IQ scores, which ischaracteristic of most of the ERP and EEG studies, could have influenced the relationshipbetween EEG parameters and intelligence.The aim of the present study was to further investigate the relationship between EEG activity

and verbal and performance components of intelligence using complex figural tasks involvingmemory and learning processes. To obtain a more clear-cut relationship between EEG para-meters and intelligence, participants were controlled for the level of creativity and emotionalintelligence. Induced, non-phase-locked brain activity resulting from changes in the functionalconnectivity within the cortex was investigated using the method of ERD/ERS. Significant dif-ferences were further investigated using the LORETA method which can estimate the extents ofthe active brain areas (Fuchs, Wagner, Kohler, & Wischmann, 1999). The method permits a directthree-dimensional tomography of brain electric activity while requiring only simple constraints, andno predetermined knowledge about the putative number of discernable source regions. Previousstudies employing LORETA have shown that this method is able to distinguish between group dif-ferences related to affective attitude (Pizzagalli, Lehmann, Koening, Rgard, & Pascual-Marqui,2000), schizophrenia (Pascual-Marqui, Lehman, Koening, Kochi, Merlo, Hell, & Koukkou., 1999),and intelligence (Jausovec & Jausovec, 2001). The efficiency theory would predict greater alphasynchronization and theta desynchronization in high intelligent individuals, as well as a morefocused (less volume involved) activity in regions that are directly related to task performance inhigh intelligent individuals as compared with low intelligent ones. A reverse pattern of ERD/ERSwould be expected following Klimesch’s suggestions. It would be also expected that high intelligentindividuals would display a higher level of SNR as compared with average intelligent individuals.

2. Method

2.1. Subjects

The sample included 28 right-handed individuals (20 females and 8 males). The participantswere student-teachers taking a course in psychology. The mean age of the sample was 20.4 years(S.D.=0.7; range 19–21). The individuals were selected from a sample of 670 individuals whowere tested with creativity tests (Wallach & Kogan, 1965), emotional intelligence tests (Mayer,Caruso, & Salovey, 2000), and with eight WAIS subtests (five verbal, and three performance).

N. Jausovec, K. Jausovec / Personality and Individual Differences 36 (2004) 597–612 599

The test results are summarized in Table 1. Two groups were formed—high intelligent (HIQ;n=14), and average intelligent (AIQ; n=14). All participants were given prior information aboutthe aims and procedures of the study.

2.2. Procedure and materials

The EEG was recorded while students were performing two memory tasks. The tasks weresimilar to those in the well known children’s game ‘Memory’. In one condition, subjects had tolearn associations between colors and locations on a check board grid. The grid had four rowsand five columns. In that way ten color pairs were presented. The same grid was presented 40times at fixed 11 s interstimulus intervals. First, the grid, showing all color pairs (e.g. first row:red, green, black, white, blue; second row: black, pink, purple, yellow, white. . .), was presented ona computer screen for 4 s (learning phase), followed by a 2-s period with no presentation (holdphase), followed by the presentation of the grid with just one color (e.g. black in the third columnof the first row), and four location options (e.g. numbers ‘1’ first row first column; ‘2’ first rowsecond column; ‘3’ second row first column; ‘4’ second row second column), for the other colorpair (recall). During this time (2 s) the students were instructed to press a button which indicatedtheir answer (for the earlier example the correct answer=3).The second task was similar to the first one, except that 10 grids with different color arrange-

ments were presented in random succession. In that way the difficulty level of the task wasincreased. All pictures were generated by the STIM stimulator (a hardware and software systemfor auditory and visual stimulus task design and presentation—Neuroscan, Sterling, VA, USA),which also recorded the responses made by individuals.

2.3. EEG recording

EEG was recorded using a Quick-Cap with sintered (Silver/Silver Chloride; 8mm diameter)electrodes. Using the Jasper (1958) Ten-twenty Electrode Placement System of the International

Table 1Means (M), standard deviations (S.D.), minimum and maximum (Min./Max.), and t-tests (t) for differences between

the average (AIQ) and high intelligence (HIQ) groups in verbal and performance IQ, emotional IQ and creativity testscores, and mean alpha peak frequency (IAF)

Ability
Group M S.D. Min./Max. t P<
Verbal and performance intelligence
AIQ 93 12.6 91/106 9.9 0.000 HIQ 128 3.1 124/134
Emotional Intelligence
AIQ 97 28.6 61/125 1.53 0.143 HIQ 110 11.0 71/128
Creativity
AIQ 47 6.8 41/63 1.46 0.159 HIQ 53 10.8 41/76
IAF
AIQ 9.14 1.15 7.5/11.3 0.53 0.600 HIQ 9.39 1.36 7.5/11.6

Federation, the EEG activity was monitored over 19 scalp locations (Fp1, Fp2, F3, F4, F7, F8,T3,T4, T5, T6, C3, C4, P3, P4, O1, O2, Fz, Cz and Pz). All leads were referenced to linked mas-toids (A1 and A2), and a ground electrode was applied to the forehead. Additionally, vertical eyemovements were recorded with electrodes placed above and below the left eye. Electrode impe-dance was maintained below 5 k�. The digital EEG data acquisition and analysis system(SynAmps) had a bandpass of 0.15–40.0 Hz. At cutoff frequencies the voltage gain was approxi-mately -6dB. The 19 EEG traces were digitized online at 1000 Hz with a gain of 1000 (resolutionof 0.084 mV/bit in a 16 bit A–D conversion), and stored on a hard disk. Epochs were comprisedfrom the 3000 ms preceding and 8000 ms following the stimulus presentation and automaticallyscreened for artifacts. Excluded were all epochs showing amplitudes above�80 mV (less than2%). The analysis was performed with the Scan 4.2 software.

2.4. The individual determination of frequency bands

Alpha frequency varies to a large extent as a function of age, neurological diseases, brainvolume and task demands (see Klimesch, 1999). Even though the participants in the present studywere homogenous in age with no neurological diseases reported, the frequency windows for thealpha and theta bands were individually determined (Klimesch, 1999; Burgess & Gruzelier, 1999).This was done to allow comparison with the Klimesch’s (Doppelmayr et al., 2002; Klimesch,1999; Klimesch & Doppelmayr, 2001) studies. For this purpose the mean alpha peak frequency(IAF) for each subject was determined. Firstly, averaged over all epochs, power spectra were cal-culated over the entire epoch length of 11 s for each lead. Secondly, peak frequency in the alphaband (7–13 Hz) was determined separately for each lead. Thirdly, IAF was obtained by averagingthe respective values over all leads. In using IAF as an individual anchor point, four different fre-quency bands with a bandwidth of 2 Hz each were determined: theta (IAF�6) to (IAF�4), lower1alpha (IAF�4) to (IAF�2), lower2 alpha (IAF�2) to IAF, and upper alpha IAF to (IAF + 2).Averaged over the sample of subjects, IAF was 9.26 Hz. The standard deviation was 1.22 Hz. Ascan be seen in Table 1, the two groups did not differ significantly in IAF.

2.5. The calculation of induced ERD/ERS

The ERD/ERS were determined using the method of complex demodulation with a simulta-neous signal envelope computation (Andrew, 1999; Otnes & Enochson, 1978; Thatcher, Toro,Pflieger, & Hallet, 1994). In this method the raw data for each channel are multiplied, point bypoint, by a pure cosine based on the selected center frequency, as well as by a pure sine having thesame center frequency. Both time series are then lowpass filtered (zero-phase digital filter �48 dB/octave rolloff) by the half-bandwidth (1 Hz). The quantification of induced ERD was done usingthe intertrial variance method [ERD (IV)—induced, non-phase-locked activity]. Induced bandpower (IBP) is an event-related variance in a frequency band. Variance is computed at each timesample across trials, and the power spectrum is computed based on the variance measures, withinthe selected frequency band. The formulas used were as follows (Pfurtscheller, 1999):

IV jð Þ ¼1

N � 1

XN

i¼1

xf i; jð Þ � xf jð Þ

� �2


e N is the total number of trials, xf (i, j) is the jth sample of ith trial of bandpass filtered data,
wherand xf jð Þ is the mean of the jth sample over all bandpass filtered trials. The ERD (IV) data wereused to calculate the ERD/ERS values which were defined as the percentage change of the powerat each sample point (Aj), relative to the average power in the resting 1000 ms reference interval(R) preceding the stimulus onset (�1500 to �500 ms):
ERD jð Þ% ¼R � Aj

R

A positive ERD indicates a power decrease, and a negative ERD a power increase (Pfurt-scheller, 1999). The ERD/ERS values were determined for seven 1000 ms time windows (fromstimulus onset till 7000 ms). Also determined were the IBP values for the 1000 ms referenceinterval.

2.6. The computation of LORETA

The data (induced band power in the theta, and upper alpha) were read into the CURRY 4.5software which performed the LORETA analysis. Current density methods are commonlyapplied to single time-point evaluations (Fuchs et al., 1999). In order to allow for comparison ofthe time course of intelligence-related differences in stimulus processing, seven individuallyassessed time points were chosen for the LORETA analysis. The time points were taken in eachof the 7 s following stimulus presentations. In each time segment the reference point was thehighest peak amplitude—the most positive point in the mean global field power (MGFP). Priorto the LORETA analysis for each subject the SNR was estimated from prestimulus latencies(�500 to �1500 ms). These SNR values were used for the regularization parameter l. Theoptimum value of l was determined using the w2 criterion. This criterion was implemented byiteratively calling the LORETA method. A three-spherical-shells volume conductor model wasapplied (radii: 75, 82 and 88 mm; conductivities: 0.33, 0.0042, and 0.33 1/�m). The LORETAreconstruction was performed on a surface net constrained to the cortex, using surface Lapla-cians (Fuchs et al., 1999). The cortical gray matter layers were segmented from the grandaverage magnetic resonance image data set (CURRY–Neuroscan, Sterling, VA, USA). Theyconsisted of 6618 vertices and 13 934 triangles (mean edge length 3.6 mm). The cortex normals,which define the orientation perpendicular to the cortical sheet—the net orientation of thesynapses of the pyramidal cells—were also calculated (Wagner, 1998). To account for theundesired depth dependency of all current density algorithms, and thus achieve an unbiasedlead-field distribution, a location wise singular value decomposition lead-field normalization wasperformed, followed by a gain determination using the infinity norm and an auto-adaptedcomponentwise depth weighting. The quantitative evaluation of the reconstruction parameterswas done by calculating the full width at half maximum (FWHM) volume (ml): that is, allcurrent positions with strengths above 50% of the maximum current were counted and thenmultiplied by the cell-volume. Also determined was the maximum current density (mAmm�2)and the source locations at the maximum current density. The source locations were given as(X,Y,Z) coordinates: X from left to right; Y from posterior to anterior; Z from inferior tosuperior.


3. Results

The data were analyzed using the statistical package SPSS 11 for Windows. All univariate repe-ated measures general linear model (GLM) analyses were corrected for violation of the sphericityassumption. The results sections include the corrected P, and df—Huynh-Feldt (Jennings, 1987).

3.1. Performance measures

The GLM for repeated measures conducted for the correct answers given by subjects whileperforming the two tasks—2 (task)�2 (group)—revealed a significant main effect of the factorgroup [F (1, 25)=15.52, P<0.001], as well as a significant group by task interaction effect[F (1, 25)=4.26, P<0.05]. As can be seen in Fig. 1, HIQ individuals scored higher on both tasksthan did AIQ individuals, however this difference was more pronounced for Task-1 than forTask-2. The correlation between IQ and Task-1 score was r=0.66 (P<0.0001), the correlationbetween IQ and Task-2 score was r=0.38 (P<0.044).

3.2. Induced ERD/ERS (learning, hold, recall), and IBP (resting) measures

The differences in ERD/ERS measures in each frequency band were determined with a GLMfor repeated measures—2 (task)�7 (time)�19 (electrode location)�2 (group). In the three alpha

Fig. 1. The number of correct answers given by HIQ and AIQ respondents while performing the two memory tasks.


bands the only significant difference obtained was in the upper alpha band, where a significantbetween-groups difference was observed [F (1, 26)=15.94, P<0.000]. As can be seen in Fig. 2, theHIQ group showed a much higher desynchronization in the upper alpha band than did the AIQgroup. This difference was quite stable, and showed no interaction with electrode position, thedifficulty level of the presented tasks, and time which was related to the cognitive processes oflearning, holding the information in working memory and recall.In the theta band the differences were less pronounced. The main effect of the factor group only

slightly missed the significance level [F (1, 26)=3.10, P<0.09]. To obtain a more detailed pictureof possible between-group differences, the data were collapsed for the learning, hold and recallprocesses and a GLM for repeated measures was conducted—3 (process)�2 (task)�2 (group). Asignificant group by process interaction effect was obtained [F (2, 52)=3.91, P<0.026]. FurtherGLMs conducted for each time interval separately showed for the 2-s interval (learning), and 7-sinterval (recall) a significant main effect of the factor group [Fs2 (1, 26)=5.67, P<0.025; F7s (1,

Fig. 2. The level of desynchronization/synchronization (%) in the upper alpha band of AIQ and HIQ respondentsduring the performance of Task-1 and Task-2, averaged for the 7-s solution periods (learning, hold and recall).


26)=6.30, P<0.019]. As can be inferred from Fig. 3, the AIQ group showed a more intensesynchronization than did the HIQ group.As stressed in the introduction, differences in ERD/ERS measures could be related to the

reference intervals. Therefore differences in resting IBP in the theta and upper alpha band wereanalyzed with a GLM for repeated measures—2 (task)�19 (electrode location). No significantgroup, or location by group interaction effects were determined.

3.3. LORETA

The differences in source locations (separately for X, Y, and Z), FWHM volume, the maximumcurrent density, and SNR for the theta and upper alpha band were determined by GLMs—2(task)�7 (time)�2 (group).In the upper alpha band, significant main effects for the factor group for the Y location [F (1,

26)=7.19, P<0.013], as well for the SNR [F (1, 26)=8.69, P<0.007], were obtained. Also sig-nificant was the group by task interaction effect for the maximum current density [F (1, 26)=6.29,P<0.019]. The source location for the HIQ group was more anterior, whereas for the AIQ groupa more posterior source location was observed (see upper part of Fig. 4). The SNR values of theHIQ group were much lower than those of the AIQ group (see lower part of Fig. 4).As can be seen in Fig. 5, the AIQ group displayed higher maximum current density than did the

HIQ group. This difference was especially pronounced for Task-2. The FWHM volume showedno significant intelligence related difference.In the theta band, it was only for the SNR values that a significant main effect of the group

factor was observed [F (1, 26)=7.56, P<0.011]. The trend of differences was the same as in theupper alpha band, i.e. the SNR values of the HIQ group were much lower than those of the AIQgroup.

4. Discussion

In the present study an endeavor was made to shed some light on the conflicting resultsobtained with the ERD/ERS method in relation to intelligence. In a series of studies, Klimesch(1999; Klimesch & Doppelmayr, 2001) found that upper alpha desynchronization was larger forgood memory performers than bad performers. In the theta band, good memory performancewas reflected by a large extent of synchronization. On the other hand, research by Jausovec andJausovec (2000a), Neubauer et al. (1995, 1999), has shown a reverse pattern of activity, support-ing the efficiency theory of intelligence. In the present study major differences in EEG activityrelated to intelligence were observed in the upper alpha band. High intelligent individuals displayedmore desynchronization than did average intelligent individuals. The LORETA analysis indi-cated that average intelligent individuals displayed greater maximum current density than didhigh intelligent individuals. This difference was significant for the more difficult second task. Asimilar pattern of EEG activity as shown by average intelligent individuals in the present study(higher current density values accompanied by more synchronization in the upper alpha band),could be observed in high intelligent individuals while performing an auditive oddball task(Jausovec & Jausovec, 2000a, 2001). In the theta band only minor differences were observed. In


Fig. 3. The level of desynchronization/synchronization (%) in the theta band of AIQ and HIQ respondents during theperformance of Task-1 and Task-2, for the 2 s (learning), and 7-s (recall) time periods.


Fig. 4. Source locations (mm) in the upper alpha band for the maximum current density (upper figure) and SNRvalues (lower figure) for the AIQ and HIQ groups of individuals.


Fig. 5. Average maximum current density (mAmm�2) in the IBP in the upper alpha band for the two memory tasks.


some time-segments of the learning and recall phases high intelligent individuals showed lesstheta synchronization than did average intelligent individuals.The EEG analysis in the present study was not sensitive to differences in task performance. This

curtails inferences of performance specific brain activity and the link to intelligence, as well as thecomparison with Klimesch’s (1999; Klimesch & Doppelmayr, 2001) studies which were basedonly on the respondents’ memory performance. However, the significant correlations between IQand task performance scores suggest that both variables are highly related and therefore allow atentative comparison between studies.A possible explanation for the conflicting findings could well lie in the type of tasks used in the

studies mentioned. As stressed by Gevins and Smith (2000), a large number of the studies have usedtasks that made only trivial demands on cognitive abilities. Another, probably more important dif-ference, is also that tasks used in Klimesch’s, and in the present study involved memory and learningprocesses. Ericsson and Kintsch (1995) mentioned at least three differences which set apart memoryand problem solving tasks. First, memory tasks minimize relevant experience; second, storage ofinformation in long-term memory (LTM) is a demanding task; and finally, it is more important thatsome information has been stored in LTM, and not that relevant information be accessible. Fromthis viewpoint it can be speculated that it is not the focused, but rather the more widespread brainactivity that would be related to good memory performance. Therefore greater event related desyn-chronization in the upper alpha band displayed by high-intelligent individuals could well point to amore ‘efficient’ task approach. Support for this hypothesis also comes from the network theories ofLTM. The strength of links between concepts is incremented every time a link is used (Anderson,1976). In neurological terms this would correspond to a Hebb synapse that has undergone a changeduring learning (Hebb, 1949). Some recent studies involving learning and memory tasks also point inthis direction. Gevins and Smith (2000) have reported, for more difficult memory tasks, a practice-related increase in theta power in high intelligent individuals. In a PET study, Habib, McIntosh,and Tulving (2000) observed that patterns of brain activity gradually shifted, with learning, fromfrontal regions to posterior regions. They hypothesized that these shifts reflected automatizationof the processes underlying task requirements. A similar finding was also observed in our study.The LORETA analysis for the upper alpha band showed that the EEG source location in highintelligent individuals was more frontal, as compared to average ones. In another PET study,Haier et al. (1992) demonstrated that the magnitude of brain activity decreased as an effect oftask practice. This effect was more pronounced in high intelligent individuals as compared toaverage ones. Haier (1993) suggested that individual differences in human intelligence may berooted in the organizational development of the brain, in neural pruning a decrease in synapticdensity from 5 years of life till early teen years. The data of the present study do not allow one tomake any conclusions as to possible changes in mental activity during a prolonged learningphase, because EEG measurements were taken only during an early learning stage.The finding that high intelligent individuals showed more desynchronization in the upper-alpha

band, and average intelligent individuals more synchronization in the theta band could well pointto differences in learning and memory strategies employed by both groups of individuals.Research has shown that episodic memory processes and working memory performance seem tobe reflected as oscillations in the EEG theta frequencies (Klimesch, 1996), whereas upper alphaactivity is modulated by semantic memory processes (Klimesch, 1997). From this viewpoint it islikely that high intelligent individuals tried to learn associations between colors and locations on


a check board grid by relying more on semantic memory processes, while average intelligentindividuals relied more on episodic memory, and/or holding the patterns in working memory. Asindicated by the analysis of correct answers given by both groups of individuals, the strategy ofhigh intelligent individuals was more efficient, especially for the first task. This could be explainedby the structure of the task. In the first task the same grid was presented forty times, therefore asemantic learning strategy (e.g. red first row, third column, and second row, fourth column) wasprobably much more efficient than in the second task, where each grid was shown only fourtimes, which made semantic learning more difficult. However, this hypothesis is rather vague,because the strategies employed by respondents were not tested. The hypothesis is mainly basedon what is known about the relationship between narrow band EEG activity and memory pro-cesses (Klimesch, 1999); as well as on research findings indicating that high intelligent individualsemploy more task relevant solution strategies accompanied by metacognitive processes (Jausovec,1994).As can be seen in Section 2.5, the quantification of ERD is dependent on band power in

the reference interval. Therefore any group differences in ERD might be due not only todifferences in cortical activation patterns during task performance but also to differences inresting power. Klimesch (1999, Klimesch & Doppelmayr, 2001) has suggested that the differ-ences between high and average intelligent individuals observed in the upper alpha and thetaband power were partly due to differences in the resting interval. In the upper alpha band ahigh power in the reference interval was predicted, whereas the opposite holds true for thetheta band, where a small reference power is related to a large amount of synchronization orincrease in power. It was further suggested that this difference is related to a physiologicalmechanism which operates to increase the SNR during task performance. The present studydid not confirm this hypothesis. No between-group differences in upper alpha and theta IBPin the resting intervals were observed. The LORETA analysis further indicted that averageintelligent individuals displayed much higher SNR values in the upper alpha and theta IBPthan did high intelligent individuals. This difference was present during the whole 7-s periodof task performance.Summarizing the findings, it can be concluded that the ‘neural efficiency hypothesis’, which

would predict greater alpha synchronization and theta desynchronization in high intelligentindividuals, has only partly received support in the present study. A tentative hypothesis is thatlearning and memory processes, especially in the early stages, require a more intense and wide-spread brain activity, and that differences related to intelligence are mainly due to the types ofstrategies employed by individuals. We speculated that high intelligent individuals relied more onsemantic memory processes, whereas low intelligent individuals relied more on episodic memory.These findings require replication, and a methodological extension, which would allow for testingthe learning strategies used by respondents.

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Intelligence related differences in induced brain activity during the performance of memory tasks

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